For today's warmup, you will be working with National Basketball Association data. This data was acquired using the nba_api package. You will not have to use the API, rather, you will work accessing data stored in dictionaries created from the data returned from the API calls.

import pickle

with open('data/nba_data.p', 'rb') as read_file:
    team_dicts = pickle.load(read_file)

The team_dicts object is a list, each element of which is a dictionary associated with a different NBA team's stats from the 2019-2020 season.

# Type of the outer object
type(team_dicts)

list

# Type of the first inner object
type(team_dicts[0])

dict

The first team listed, i.e. the team dictionary associated with index 0 of team_dicts, is the LA Lakers.

lakers = team_dicts[0]

# Take a look at the content of the lakers dictionary
lakers

{'LeagueID': '00',
 'SeasonID': '22019',
 'TeamID': 1610612747,
 'TeamCity': 'Los Angeles',
 'TeamName': 'Lakers',
 'Conference': 'West',
 'ConferenceRecord': '36-10',
 'PlayoffRank': 1,
 'ClinchIndicator': ' - w',
 'Division': 'Pacific',
 'DivisionRecord': '10-3 ',
 'DivisionRank': 1,
 'WINS': 52,
 'LOSSES': 19,
 'WinPCT': 0.732,
 'LeagueRank': 0,
 'Record': '52-19',
 'HOME': '25-10',
 'ROAD': '27-9 ',
 'L10': '4-6  ',
 'Last10Home': '7-3  ',
 'Last10Road': '6-4  ',
 'OT': '2-0  ',
 'ThreePTSOrLess': '7-3  ',
 'TenPTSOrMore': '25-11',
 'LongHomeStreak': 6,
 'strLongHomeStreak': 'W 6',
 'LongRoadStreak': 14,
 'strLongRoadStreak': 'W 14',
 'LongWinStreak': 10,
 'LongLossStreak': 4,
 'CurrentHomeStreak': -1,
 'strCurrentHomeStreak': 'L 1',
 'CurrentRoadStreak': -2,
 'strCurrentRoadStreak': 'L 2',
 'CurrentStreak': -1,
 'strCurrentStreak': 'L 1',
 'ConferenceGamesBack': 0.0,
 'DivisionGamesBack': 0.0,
 'ClinchedConferenceTitle': 1,
 'ClinchedDivisionTitle': 1,
 'ClinchedPlayoffBirth': 1,
 'EliminatedConference': 0,
 'EliminatedDivision': 0,
 'AheadAtHalf': '39-8 ',
 'BehindAtHalf': '11-10',
 'TiedAtHalf': '2-1  ',
 'AheadAtThird': '43-0 ',
 'BehindAtThird': '7-16 ',
 'TiedAtThird': '2-3  ',
 'Score100PTS': '48-14',
 'OppScore100PTS': '36-19',
 'OppOver500': '22-14',
 'LeadInFGPCT': '42-4 ',
 'LeadInReb': '38-7 ',
 'FewerTurnovers': '33-5 ',
 'PointsPG': 113.4,
 'OppPointsPG': 107.6,
 'DiffPointsPG': 5.8,
 'vsEast': '16-9',
 'vsAtlantic': '5-5',
 'vsCentral': '4-3',
 'vsSoutheast': '7-1',
 'vsWest': '36-10',
 'vsNorthwest': '12-3',
 'vsPacific': '10-3',
 'vsSouthwest': '14-4',
 'Jan': '10-4',
 'Feb': '9-2',
 'Mar': '4-1',
 'Apr': None,
 'May': None,
 'Jun': None,
 'Jul': '1-0',
 'Aug': '2-5',
 'Sep': None,
 'Oct': '3-1',
 'Nov': '14-1',
 'Dec': '9-5',
 'PreAS': '41-12',
 'PostAS': '11-7'}

Dictionaries are made of key:value pairs. You can see the keys of a dictionary using the .keys() method.

# List all the keys of the lakers dictionary
# your code here

You can see the values of a dictionary using the .values() method.

# List all the values of the lakers dictionary
# your code here

Task 1

# Assign the Laker's Division Rank to the variable division rank

division_rank = None

Task 2

Now using the team_dict list, loop through each team dictionary and print out the team name and the division rank for the 2019-20. The output of the first round of the loop should look like:

Lakers 1

# Your code here

Task 3

Next, let's create a set of dictionaries nested within a dictionary.

Each dictionary will represent a division, and each value will be another dictionary.

The second level dictionaries will have team names as keys, and division rank as values.

So, one key:value pair of the outer dictionary would look like so:

'Pacific': {'Lakers': 1, 'Clippers': 2, 'Suns': 3, 'Kings': 4, 'Warriors': 5}

# First step, get a list of all the division names
division_names = [team['Division'] for team in team_dicts]

# What you see above is a list comprehension.  It does the same thing as below.
division_names = []

for team in team_dicts:
    division_names.append(team['Division'])

Once you get the hang of them, list comprehensions are amazing. They are visually concise and faster than explicit for loops.

# We can use set to get the unique values
division_names = list(set(division_names))
division_names

['Central', 'Southwest', 'Southeast', 'Atlantic', 'Northwest', 'Pacific']

Now your turn. Populate a dictionary called division_dictionary that has the division ranks of each team in each of the 6 divisions.

Hint: you may need two for loops, one nested within the other. A dictionary for each division should be created between the two for loops.

# Your code here
division_dictionary = None

Bonus

The original API call is sort of messy. With the .get_dict() method associated with the nba_api package, we are left with a nested series of lists and dictionary. To get the data, we need to parse it with a series of keys and list indices.

The bonus task asks you to recreate the exercise from task 3 above, starting with the new_response object below. I.E., create a division dictionary for the 2018-19 season.

If you are feeling adventurous, code a function that takes the response as an argument, and returns the dicitonary.

The first key: value pair should be:

'Central': {'Bucks': 1, 'Pacers': 2, 'Pistons': 3, 'Bulls': 4, 'Cavaliers': 5},

new_response= leaguestandings.LeagueStandings(season='2018-19').get_dict()

# Your code here

sbaltodano/dsc-dictionary_warmup

Task 1

Task 2

Task 3

Bonus